The Internet is being increasingly used to search for and view images (e.g., photographs). To support this use, commercial search engine services have located and indexed over 1 billion images since 2005. Users who search for images often want to locate and view images of high quality. For example, a user interested in nature may want to view and purchase nature-related photographs that are of high quality. After locating images that match a query, these search engines typically display the located images in an order that is based on relevance of the image to the query. These search engines may calculate relevance based on similarity between the terms of the query and metadata (e.g., title) associated with the located images. Although domain-specific or vertical search engines may be effective at locating images in a particular domain (e.g., nature), these search engines may have no practical way of determining the quality of the images. Thus, a user may need to view many pages of images before finding a high-quality image that is of interest to the user.
The quality of an image can be rated either subjectively (e.g., manually) or objectively (e.g., automatically). A person can subjectively rate the quality of an image by viewing the image and assigning a rating of, for example, between 1 and 10. Because of the vast resources that would be needed to manually rate a billion images, a subjective rating of each image is currently impracticable. Various algorithms have been developed to objectively rate images. Some simple rating algorithms focus on attributes such as image size and color versus black and white to rate the quality of an image. Such simple objective rating algorithms do not provide an “artistic” or “aesthetic” quality rating as provided by a subjective rating. Some objective rating algorithms assess the content of an image to provide a more artistic quality rating. Most of these algorithms, however, are based on colorfulness, contrast, and sharpness analysis. These algorithms fall short of providing an artistic quality rating comparable to a person's subjective rating.
Search engines typically display the images of a search result as thumbnails in a grid form. A user can locate an image of interest by scrolling through the grid viewing the thumbnails. When the user selects an image that may be of interest, an enlarged version of the image may be displayed in a separate window. After viewing the enlarged version of the image, the user may decide to download the image. A user who is looking for many images may store the downloaded images in a folder on a local storage device. There are, however, several disadvantages to such a user interface for viewing and then downloading images. First, as discussed above, because the thumbnails are ordered by relevance to the input query, a user may need to browse many pages of thumbnails to find a high-quality image of interest. Second, the thumbnails may be too small to allow the user to effectively assess the quality of the images. As a result, the user may need to repeat the process of selecting a thumbnail from a window containing the grid of thumbnails, viewing the selected image in a separate window, and changing the focus back to the grid. In addition, once a user decides to download an image, the user needs to save the image to the appropriate folder. The process of selecting thumbnails and viewing images in separate windows can be time-consuming and distracting to the user, who may not be able to effectively assess and remember the relative quality of the different images.